Abstract
A Neural Network is treated as a data transformer when used for mapping purposes. The objective in this case, is to associate the elements in one set of data with the elements in a second set. According to this principle, three encoding methods, namely, single output layer, binary encoding, and ortho-encoding, have been designed for the output layer of a Neural Network based on five criteria, and put into experiments for Remote Sensing classification by means of a series of images coordinated with incremental noise level, from 1% to 10%. At last, the experiment results are assessed from different perspectives such as, accuracy, convergence, mixture detection, and confidence of classification, comparing to three encoding methods respectively.
Original language | English |
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Pages (from-to) | 254-260 |
Number of pages | 7 |
Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
Volume | 33 |
State | Published - 1 Jan 2000 |
Event | 19th International Congress for Photogrammetry and Remote Sensing, ISPRS 2000 - Amsterdam, Netherlands Duration: 16 Jul 2000 → 23 Jul 2000 |
Keywords
- Classification
- Neural network
- Output layer architecture
- Remote sensing
ASJC Scopus subject areas
- Information Systems
- Geography, Planning and Development